{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/image-synthesis-in-multi-contrast-mri-with","title":"Image Synthesis in Multi-Contrast MRI with Conditional Generative Adversarial Networks","arxiv_id":"1802.01221","date":"2018-02-05","proceeding":null,"authors":["Salman Ul Hassan Dar","Mahmut Yurt","Levent Karacan","Aykut Erdem","Erkut Erdem","Tolga Çukur"],"abstract":"Acquiring images of the same anatomy with multiple different contrasts\nincreases the diversity of diagnostic information available in an MR exam. Yet,\nscan time limitations may prohibit acquisition of certain contrasts, and images\nfor some contrast may be corrupted by noise and artifacts. In such cases, the\nability to synthesize unacquired or corrupted contrasts from remaining\ncontrasts can improve diagnostic utility. For multi-contrast synthesis, current\nmethods learn a nonlinear intensity transformation between the source and\ntarget images, either via nonlinear regression or deterministic neural\nnetworks. These methods can in turn suffer from loss of high-spatial-frequency\ninformation in synthesized images. Here we propose a new approach for\nmulti-contrast MRI synthesis based on conditional generative adversarial\nnetworks. The proposed approach preserves high-frequency details via an\nadversarial loss; and it offers enhanced synthesis performance via a pixel-wise\nloss for registered multi-contrast images and a cycle-consistency loss for\nunregistered images. Information from neighboring cross-sections are utilized\nto further improved synthesis quality. Demonstrations on T1- and T2-weighted\nimages from healthy subjects and patients clearly indicate the superior\nperformance of the proposed approach compared to previous state-of-the-art\nmethods. Our synthesis approach can help improve quality and versatility of\nmulti-contrast MRI exams without the need for prolonged examinations.","url_abs":"http://arxiv.org/abs/1802.01221v1","url_pdf":"http://arxiv.org/pdf/1802.01221v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"image-synthesis-in-multi-contrast-mri-with","repo_url":"https://github.com/icon-lab/pGAN-cGAN","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":{"status":"unanswered"}},{"paper_slug":"image-synthesis-in-multi-contrast-mri-with","repo_url":"https://github.com/CV-Reimplementation/pGAN-Reimplementation","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":{"status":"unanswered"}}],"tasks":[{"task_slug":"anatomy","task_name":"Anatomy"},{"task_slug":"diagnostic","task_name":"Diagnostic"},{"task_slug":"image-generation","task_name":"Image Generation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1802.01221","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}